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- W4293722105 endingPage "118624" @default.
- W4293722105 startingPage "118624" @default.
- W4293722105 abstract "This paper presents a comprehensive survey of data-driven robotic visual grasping detection (DRVGD) for unknown objects. We review both object-oriented and scene-oriented aspects, using the DRVGD for unknown objects as a guide. Object-oriented DRVGD aims for the physical information of unknown objects, such as shape, texture, and rigidity, which can classify objects into conventional or challenging objects. Scene-oriented DRVGD focuses on unstructured scenes, which are explored in two aspects based on the position relationships of object-to-object, grasping isolated or stacked objects in unstructured scenes. In addition, this paper provides a detailed review of associated grasping representations and datasets. Finally, the challenges of DRVGD and future directions are pointed out." @default.
- W4293722105 created "2022-08-31" @default.
- W4293722105 creator A5010927997 @default.
- W4293722105 creator A5022564470 @default.
- W4293722105 creator A5041685561 @default.
- W4293722105 creator A5043667109 @default.
- W4293722105 creator A5055290886 @default.
- W4293722105 creator A5087396510 @default.
- W4293722105 date "2023-01-01" @default.
- W4293722105 modified "2023-10-17" @default.
- W4293722105 title "Data-driven robotic visual grasping detection for unknown objects: A problem-oriented review" @default.
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